code: def conditional_householder(
      input_dim, context_dim, hidden_dims=None, count_transforms=1
  ):
      """
      A helper function to create a
      :class:`~pyro.distributions.transforms.ConditionalHouseholder` object that takes
      care of constructing a dense network with the correct input/output dimensions.
  
      :param input_dim: Dimension of input variable
      :type input_dim: int
      :param context_dim: Dimension of context variable
      :type context_dim: int
      :param hidden_dims: The desired hidden dimensions of the dense network. Defaults
          to using [input_dim * 10, input_dim * 10]
      :type hidden_dims: list[int]
  
      """
  
      if hidden_dims is None:
          hidden_dims = [input_dim * 10, input_dim * 10]
      nn = DenseNN(
          context_dim,
          hidden_dims,
          param_dims=[input_dim] * count_transforms,
      )
      return ConditionalHouseholder(input_dim, nn, count_transforms)
  
imports: import math
  import warnings
  from functools import partial
  import torch
  import torch.nn
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 2 for calling a global function with example usage: conditional_householder(input_dim, context_dim, hidden_dims, count_transforms) and returns: ConditionalHouseholder